A control system of a driverless vehicle relies on sensors to sense the ambient environment. To obtain more abundant and comprehensive information, the driverless vehicle is generally equipped with multiple sensor devices. Among the sensors, a laser radar is responsible for the most important functions such as vehicle locating and obstacle detection. At present, different laser radars are respectively disposed at a plurality of different positions of the driverless vehicle, and the laser radars at different positions cover different viewing angles. To achieve a better control effect, the control system of the driverless vehicle integrates data collected by a plurality of laser radars.
At present, mainly the following two integration methods are used in the industry:
(1) Integration based on preprocessed point cloud data: Respective laser radars scan surrounding objects, after the laser radars rotate a complete cycle, unpacking and compensation processing is performed on all points collected to generate a plurality of point cloud data pieces, and the plurality of point cloud data pieces is then integrated into a new point cloud data piece. Because the laser radars are started at different time points, an offset of up to one cycle in the time domain exists between point cloud data collected by different laser radars, leading to a serious ghosting phenomenon of the integrated point cloud data. As a result, the control system of the driverless vehicle has a serious control error, threatening the safety of the vehicle occupants.
(2) Integration after multiple channels of point cloud data are calculated respectively: The method of calculating multiple channels of point cloud data respectively refers to performing calculation for point cloud data collected by each of the laser radars and then integrating the calculation results, rather than directly integrating point cloud data obtained through scanning by several laser radars. Taking obstacle detection for example, obstacle detection is performed by using scan results of a plurality of laser radars, and then the obstacle detection results are deduplicated. The advantage of this solution is that the channels of data are not directly integrated, thereby avoiding the ghosting problem caused by unsynchronized data. However, because different laser radars have different collection ranges and angles and the data of the different laser radars needs to be integrated in order to obtain comprehensive information, processing the data separately may lead to insufficient information regarding the characteristic value included in point cloud data outputted by some laser radars, thereby greatly reducing the recognition accuracy of the algorithm.
Therefore, the conventional technology of integrating multiple channels of laser radar data has the ghosting problem caused by unsynchronized data, or the problem of low recognition accuracy of the algorithm due to insufficient information regarding the characteristic value.